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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.26

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2025-06-27, 07:43 UTC based on data in: /workspace/work/83/93a0f0571c373d28742999f8601cf9/results


        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant.URL: https://github.com/bigbio/pmultiqc

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.

        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

        Showing 0/1 rows and 5/5 columns.
        Sample NameMSstats ConditionMSstats BioReplicateFraction GroupFractionLabel
         
        1
        dorsolateral prefrontal cortex1
         
         ↳ e0590_01_LysConly
        111

        MS1 Information

        MS1 quality control information extracted from the spectrum files

        This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples. The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point. Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column. Key aspects to assess include:

        • Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility.
        • Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance.
        • Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity.
        • Chromatographic resolution: Sharp, well-separated peaks indicate effective separation.
        • Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues.

        Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.

        Created with MultiQC

        MS1 base peak chromatograms extracted from the spectrum files

        The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed intensity of all ions, the BPC highlights the strongest signals, providing better visualization of compounds with high abundance while reducing baseline noise. This makes it particularly useful for identifying major components in complex samples, monitoring dominant species, and providing clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across samples allows you to evaluate consistency in the detection of high-abundance compounds and can reveal significant variations in sample composition or instrument performance.

        Created with MultiQC

        MS1 Peaks from the spectrum files

        This plot shows the number of peaks detected in MS1 scans over the course of each sample run. The x-axis represents retention time (in minutes), while the y-axis displays the number of distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral complexity and provides insight into instrument performance during the LC-MS analysis. Key aspects to consider include:

        • Overall pattern: Peak counts typically increase during the elution of complex mixtures and decrease during column washing or re-equilibration phases.
        • Peak density: Higher counts suggest more complex spectra, potentially indicating a greater number of compounds present at that time point."
        • Peak Consistency across samples: Similar profiles among replicates or related samples indicate good analytical reproducibility.
        • Sudden drops: Abrupt decreases in peak count may point to transient ionization issues, spray instability, or chromatographic disruptions.
        • Baseline values: The minimum peak count observed reflects the level of background noise or instrument sensitivity in the absence of eluting compounds.

        Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram (BPC) data, offering an additional layer of quality control related to signal complexity, instrument stability, and sample composition.

        Created with MultiQC

        General stats for MS1 information extracted from the spectrum files

        This table presents general statistics for MS1 information extracted from mass spectrometry data files." It displays MS runs with their acquisition dates and times. For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities). These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2), allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation, instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.

        Showing 0/1 rows and 3/3 columns.
        FileAcquisition Date Timelog10(Total Current)log10(Scan Current)
        e0590_01_LysConly
        2014-09-28 17:09:14
        13.3340
        11.5994

        HeatMap

        This heatmap provides an overview of the performance of the quantms.

        This plot shows the pipeline performance overview. Some metrics are calculated.

        • Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
        • Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25.
        • Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544).
        • Heatmap score [Missed Cleavages]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
        • Heatmap score [Missed Cleavages Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study.
        • Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
        • Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
        • Heatmap score [Pep Missing Values]: Linear scale of the fraction of missing peptides.
        Created with MultiQC

        Summary Table

        This table shows the quantms pipeline summary statistics.

        This table shows the quantms pipeline summary statistics.

        Showing 0/1 rows and 5/5 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified#Proteins Quantified
        38056
        7283
        19.14%
        4780
        3405
        2414

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result.

        Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

        Showing 0/1 rows and 6/6 columns.
        Sample NameMSstats_ConditionFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
         
        1
        dorsolateral prefrontal cortex
         
         ↳ e0590_01_LysConly
        1
        5325
        1422
        2047
        3351

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in quantms pipeline final result

        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

        Created with MultiQC

        Distribution of precursor charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.

        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

        Created with MultiQC

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment.

        This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).

        Created with MultiQC

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.

        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

        Created with MultiQC

        Oversampling Distribution

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.

        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

        Created with MultiQC

        Delta Mass

        This chart represents the distribution of the relative frequency of experimental precursor ion mass (m/z) - theoretical precursor ion mass (m/z).

        Mass deltas close to zero reflect more accurate identifications and also that the reporting of the amino acid modifications and charges have been done accurately. This plot can highlight systematic bias if not centered on zero. Other distributions can reflect modifications not being reported properly. Also it is easy to see the different between the target and the decoys identifications.

        Created with MultiQC

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mainly the mzTab file).

        The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms.

        • BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets.
        • Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
        • Peptide intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately.
        Showing 0/50 rows and 5/5 columns.
        PeptideIDProtein NamePeptide SequenceBest Search ScoreAverage Intensitydorsolateral prefrontal cortex
        1
        uc001zkq.2
        AAAAAAAAAPAAAATAATTAATTAATAAQ
        0.9998
        9.2669
        9.2669
        2
        uc010puk.1
        AAASGNENIQPPPLAYK
        0.9998
        8.3290
        8.3290
        3
        uc003prm.3
        AADLFESEGAK
        0.9985
        7.8170
        7.8170
        4
        uc002knm.3;uc010wzj.2
        AAEALVREAEAK
        0.9998
        7.7165
        7.7165
        5
        uc003xfa.3
        AAEDEEVPAFFK
        0.9998
        9.7066
        9.7066
        6
        uc002upb.4;uc002upc.4
        AAEDLFVNIRGYNK
        0.9998
        8.4683
        8.4683
        7
        uc001avm.4
        AAELDHHWVAK
        0.9959
        7.6733
        7.6733
        8
        uc002may.3
        AAFERESDVPLK
        0.9966
        7.8756
        7.8756
        9
        uc031yrm.1
        AAFNSGK
        0.9978
        7.6067
        7.6067
        10
        uc003lmg.4;uc003lmh.4
        AAGGIELFVGGIGPDGHIAFNEPGSSLVSRTRVK
        0.9998
        8.6535
        8.6535
        11
        uc003lmg.4;uc003lmh.4
        AAGGIELFVGGIGPDGHIAFNEPGSSLVSRTRVK
        0.9998
        9.3287
        9.3287
        12
        uc003mde.1
        AALEQPCEGSLTRPK
        0.9998
        10.2247
        10.2247
        13
        uc001ojd.3
        AALTSALSIQNYHLECTETQAWMREK
        0.9998
        8.1049
        8.1049
        14
        uc001hyx.3
        AAMPRIYELAAGGTAVGTGLNTRIGFAEK
        0.9998
        8.7891
        8.7891
        15
        uc003thy.4
        AANGVVLATEK
        0.9998
        9.1786
        9.1786
        16
        uc003xwb.4;uc003xwc.4;uc011les.2
        AANLYASSPHSDFLDYVSAPIGK
        0.9998
        8.0116
        8.0116
        17
        uc002avr.3;uc002avs.3;uc002avt.3;uc010bjc.3
        AAPDITGHK
        0.9998
        9.5610
        9.5610
        18
        uc001nre.3;uc009ynm.1
        AAPGDRTMLDSLWAAGQELQAWK
        0.9998
        8.0166
        8.0166
        19
        uc002lyj.2;uc010xhq.2;uc010xhr.2
        AAPTEVLSMTAQPGPGHGK
        0.9998
        8.3691
        8.3691
        20
        uc002lyj.2;uc010xhq.2;uc010xhr.2
        AAPTEVLSMTAQPGPGHGK
        0.9998
        9.4502
        9.4502
        21
        uc001ucg.2;uc001uch.1;uc001uci.1;uc001ucj.1
        AAQTAEDAMQIMEQMTK
        0.9998
        7.8379
        7.8379
        22
        uc001sht.3;uc001shu.2;uc001shv.4
        AARAARFGISSVPTK
        0.9930
        8.6599
        8.6599
        23
        uc002qdy.3;uc002qea.3;uc002qeb.3
        AARELLTLDEK
        0.9990
        8.6773
        8.6773
        24
        uc002uvo.5;uc002uvq.4;uc010fst.4;uc010zhc.2
        AARFSCDIEQLK
        0.9998
        8.2148
        8.2148
        25
        uc003zxt.2
        AASAAQRELVAQGK
        0.9998
        8.8486
        8.8486
        26
        uc002yta.1;uc002ytb.1;uc002ytj.2
        AASELYIETEK
        0.9998
        8.4792
        8.4792
        27
        uc002sfr.4;uc002sfs.4
        AASGFNAMEDAQTLRK
        0.9998
        7.8218
        7.8218
        28
        uc002sfr.4;uc002sfs.4
        AASGFNAMEDAQTLRK
        0.9998
        8.7087
        8.7087
        29
        uc001pwe.4
        AASGSLHK
        0.9963
        8.1913
        8.1913
        30
        uc001dta.3;uc010oug.2
        AASLGLLQFPILNASVDENCQNITYK
        0.9998
        8.2496
        8.2496
        31
        uc003qoe.4;uc003qof.4;uc003qog.4
        AATEVSK
        0.9967
        8.5645
        8.5645
        32
        uc003kxr.2;uc003kxs.1;uc003kxt.2;uc003kxu.2;uc003kxv.2;uc011cxi.1
        AAVEALQSQALHATSQQPLRK
        0.9998
        8.6080
        8.6080
        33
        uc002alb.4
        AAVPTVSDQAAAMQLSQCAK
        0.9998
        8.0703
        8.0703
        34
        uc002cfv.4
        AAWGKVGAHAGEYGAEALERMFLSFPTTK
        0.9998
        8.8873
        8.8873
        35
        uc003nrm.3;uc011dmr.2
        AAYDIEVNTRRAQADLAYQLQVAK
        0.9998
        7.6362
        7.6362
        36
        uc004ajf.1
        AAYLQETGKPLDETLK
        0.9998
        8.6137
        8.6137
        37
        uc033bkh.1
        AAYLSDPRAPPCEYK
        0.9977
        8.9755
        8.9755
        38
        uc031tmu.1
        ACADATLSQITNNIDPVGRIQMR
        0.9998
        8.9962
        8.9962
        39
        uc031tmu.1
        ACADATLSQITNNIDPVGRIQMR
        0.9998
        8.1603
        8.1603
        40
        uc004ecz.4;uc011mqq.2
        ACANPAAGSVILLENLRFHVEEEGK
        0.9998
        9.3165
        9.3165
        41
        uc003ing.2;uc003inh.2
        ACCVFIFQPNGK
        0.9998
        8.9424
        8.9424
        42
        uc004dfb.3
        ACELCPEEVK
        0.9982
        7.8100
        7.8100
        43
        uc004caf.2;uc004cag.4;uc004cah.4;uc004cai.4;uc010mzf.3
        ACFQVGTSEEMK
        0.9982
        7.7785
        7.7785
        44
        uc003uwb.3
        ACGDSTLTQITAGLDPVGRIQMR
        0.9998
        8.1049
        8.1049
        45
        uc002ffg.1
        ACGLNFIDLMVRQGNIDNPPK
        0.9998
        7.8343
        7.8343
        46
        uc003hbt.1;uc010ihd.1;uc011cac.1
        ACGNFGIPCELRVTSAHK
        0.9950
        8.4391
        8.4391
        47
        uc001imh.3;uc001imk.3;uc010qbs.2;uc010qbt.2
        ACGNMFGLMHGTCPETSGGLLICLPREQAARFCAEIK
        0.9998
        7.8014
        7.8014
        48
        uc001owe.2;uc001owf.2
        ACGVDYEVK
        0.9998
        8.8800
        8.8800
        49
        uc001vhq.1;uc001vhr.1;uc010aec.1
        ACIEPCTSTK
        0.9985
        7.4364
        7.4364
        50
        uc003bpq.3;uc003bpr.3
        ACNLNLIGRPSTEHSWFPGYAWTVAQCK
        0.9998
        8.1335
        8.1335

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).

        The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run.

        • Peptides_Number: The number of peptides for each protein.
        • Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
        • Protein intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of peptides.
        Showing 0/50 rows and 4/4 columns.
        ProteinIDProtein NameNumber of PeptidesAverage Intensitydorsolateral prefrontal cortex
        1
        uc001acj.4
        1
        7.4680
        7.4680
        2
        uc001afz.2;uc001aga.2;uc001agb.2
        1
        8.4217
        8.4217
        3
        uc001alu.3;uc001alx.2
        1
        8.8279
        8.8279
        4
        uc001amd.3
        1
        9.2135
        9.2135
        5
        uc001amq.3;uc001amr.3;uc001ams.3;uc001amt.3
        4
        9.6902
        9.6902
        6
        uc001aof.2;uc001aog.2;uc010nzu.1
        1
        8.7018
        8.7018
        7
        uc001aol.3;uc010cnt.1
        1
        7.4906
        7.4906
        8
        uc001aox.4
        1
        10.0028
        10.0028
        9
        uc001aqh.3;uc001aqi.3;uc010oag.2
        2
        8.9304
        8.9304
        10
        uc001aql.1
        1
        8.2367
        8.2367
        11
        uc001aqq.3
        1
        8.5858
        8.5858
        12
        uc001aqx.4;uc001aqy.3;uc001aqz.3;uc001ara.3;uc001arb.3
        1
        7.2699
        7.2699
        13
        uc001arc.3;uc010oak.2
        3
        9.7102
        9.7102
        14
        uc001art.3;uc010oap.2
        1
        8.0168
        8.0168
        15
        uc001arz.1
        3
        8.8165
        8.8165
        16
        uc001asc.3;uc001asd.3
        1
        8.7102
        8.7102
        17
        uc001asl.3;uc010oas.2
        1
        8.1519
        8.1519
        18
        uc001avm.4
        1
        7.6733
        7.6733
        19
        uc001azt.2;uc001azu.2
        1
        7.4269
        7.4269
        20
        uc001bae.4
        1
        9.0765
        9.0765
        21
        uc001bbi.3
        1
        8.5998
        8.5998
        22
        uc001bbo.4;uc001bbp.4;uc001bbq.4
        1
        8.6133
        8.6133
        23
        uc001bce.4;uc010ocz.2
        1
        7.7966
        7.7966
        24
        uc001bge.3;uc001bgf.3;uc009vqj.1;uc010odu.2
        1
        8.0998
        8.0998
        25
        uc001bgk.2;uc001bgl.3;uc001bgm.1
        2
        8.7256
        8.7256
        26
        uc001bhk.4;uc001bhl.4
        1
        8.6948
        8.6948
        27
        uc001bht.3
        1
        8.0034
        8.0034
        28
        uc001bie.4
        1
        7.5400
        7.5400
        29
        uc001bjo.2
        1
        9.5817
        9.5817
        30
        uc001blb.3;uc001blc.3;uc010oev.2
        1
        7.9719
        7.9719
        31
        uc001bmt.1;uc001bmu.1;uc001bmv.1;uc001bmw.1;uc003qqn.3;uc003qqo.3;uc003qqp.3
        1
        7.8402
        7.8402
        32
        uc001bng.2
        4
        9.3713
        9.3713
        33
        uc001bov.2;uc001bow.2;uc021ojy.1
        1
        8.2401
        8.2401
        34
        uc001bpe.2;uc010ofp.2
        1
        8.8815
        8.8815
        35
        uc001bpn.3
        1
        8.2362
        8.2362
        36
        uc001brg.2;uc001brh.2;uc001bri.2;uc001brj.2;uc001brk.3;uc001brl.2;uc001brm.2;uc009vtk.2;uc009vtl.2;uc009vtm.2;uc021okg.1
        3
        8.9125
        8.9125
        37
        uc001brp.2;uc001brq.2;uc001brt.2;uc010ofz.2
        1
        8.4829
        8.4829
        38
        uc001bso.3;uc010oge.2
        1
        8.1781
        8.1781
        39
        uc001bss.1
        4
        8.9609
        8.9609
        40
        uc001btx.2;uc001bty.2
        1
        8.4447
        8.4447
        41
        uc001bua.2;uc001bub.4
        1
        8.7367
        8.7367
        42
        uc001buh.3
        1
        7.4655
        7.4655
        43
        uc001buj.3
        1
        7.7376
        7.7376
        44
        uc001bvh.4;uc001bvi.3;uc010ohg.2;uc010ohh.2
        1
        7.8810
        7.8810
        45
        uc001bvy.1
        4
        9.1754
        9.1754
        46
        uc001bwh.3
        1
        8.6936
        8.6936
        47
        uc001bwo.2;uc001bwp.2;uc001bwq.2;uc009vud.2;uc010ohq.2;uc010ohr.2
        2
        9.0501
        9.0501
        48
        uc001bys.3
        2
        9.3774
        9.3774
        49
        uc001bzt.3
        1
        8.7955
        8.7955
        50
        uc001bzz.4;uc001caa.4
        1
        8.5033
        8.5033

        bigbio/quantms Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/bigbio/quantms

        Methods

        Data was processed using bigbio/quantms v1.6.0dev (doi: 10.5281/zenodo.7754148) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.4 (Di Tommaso et al., 2017) with the following command:

        nextflow run bigbio/quantms -r dev --input /workspaces-streamlit-template/default/sdrf-files/sdrf.tsv --database /workspaces-streamlit-template/default/fasta-files/E05-90.fasta -profile docker --add_decoys --skip_post_msstats

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        COMETCometnull
        CometAdapter3.4.0-pre-HEAD-2025-01-20
        GENERATE_DECOY_DATABASEDecoyDatabase3.4.0-pre-HEAD-2025-01-20
        ID_FILTERIDFilter3.4.0-pre-HEAD-2025-01-20
        MZML_STATISTICSquantms-utils0.0.23
        PERCOLATORPercolatorAdapter3.4.0-pre-HEAD-2025-01-20
        percolator3.05.0, Build Date May 19 2020 06:55:29
        PROTEOMICSLFQProteomicsLFQ3.4.0-pre-HEAD-2025-01-20
        SAMPLESHEET_CHECKquantms-utils0.0.23
        SDRF_PARSINGsdrf-pipelines0.0.32
        THERMORAWFILEPARSERThermoRawFileParser1.3.4
        WorkflowNextflow25.04.4
        bigbio/quantmsv1.6.0dev-gadc69be

        bigbio/quantms Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/bigbio/quantms

        Input/output options

        export_decoy_psm
        true
        input
        /workspaces-streamlit-template/default/sdrf-files/sdrf.tsv

        SDRF validation

        skip_factor_validation
        true
        use_ols_cache_only
        true
        validate_ontologies
        true

        Protein database

        add_decoys
        true
        database
        /workspaces-streamlit-template/default/fasta-files/E05-90.fasta

        Modification localization

        luciphor_debug
        0

        PSM re-scoring (general)

        run_fdr_cutoff
        0.10

        PSM re-scoring (Percolator)

        description_correct_features
        0

        Consensus ID

        consensusid_considered_top_hits
        0
        min_consensus_support
        0

        Isobaric analyzer

        quant_activation_method
        HCD

        Protein Quantification (LFQ)

        feature_with_id_min_score
        0.10

        Statistical post-processing

        contrasts
        pairwise
        skip_post_msstats
        true

        Quality control

        enable_pmultiqc
        true
        pmultiqc_idxml_skip
        true

        Generic options

        trace_report_suffix
        2025-06-27_06-30-14

        Core Nextflow options

        configFiles
        N/A
        containerEngine
        docker
        launchDir
        /workspace
        profile
        docker
        projectDir
        /root/.nextflow/assets/bigbio/quantms
        revision
        dev
        runName
        silly_ramanujan
        userName
        root
        workDir
        /workspace/work